Tagged: Chris Tillman

The other day, I discussed predicting pitchers’ strikeout rates using xK%. I will conduct the same exercise today in regard to predicting walks. Using my best intuition, I want to see how well a pitcher’s walk rate (BB%) actually correlates with what his walk rate should be (expected BB%, henceforth “xBB%”). Similarly to xK%, I used my intuition to best identify reliable indicators of a pitcher’s true walk rate using readily available data.

An xBB% metric, like xK%, would not only if a pitcher perennially over-performs (or under-performs) his walk rate but also if he happened to do so on a given year. This article will conclude by looking at how the difference in actual and expected walk rates (BB – xBB%) varied between 2014 and career numbers, lending some insight into the (un)luckiness of each pitcher.

Courtesy of FanGraphs, I constructed another set of pitching data spanning 2010 through 2014. This time, I focused primarily on what I thought would correlate with walk rate: inability to pitch in the zone and inability to incur swings on pitches out of the zone. I also throw in first-pitch strike rate: I predict that counts that start with a ball are more likely to end in a walk than those that start with a strike. Because FanGraphs’ data measures ability rather than inability — “Zone%” measures how often a pitcher hits the zone; “O-Swing%” measures how often batters swing at pitches out of the zone; “F-Strike%” measures the rate of first-pitch strikes — each variable should have a negative coefficient attached to it.

I specify a handful of variations before deciding on a final version. Instead of using split-season data (that is, each pitcher’s individual seasons from 2010 to 2014) for qualified pitchers, I use aggregated statistics because the results better fit the data by a sizable margin. This surprised me because there were about half as many observations, but it’s also not surprising because each observation is, itself, a larger sample size than before.

At one point, I tried creating my own variable: looks (non-swings) at pitches out of the zone. I created a variable by finding the percentage of pitches out of the zone (1 – Zone%) and multiplied it by how often a batter refused to swing at them (1 – O-Swing%). This version of the model predicted a nice fit, but it was slightly worse than leaving the variables separated. Also, I ran separate-but-equal regressions for PITCHf/x data and FanGraphs’ own data. The PITCHf/x data appeared to be slightly more accurate, so I proceeded using them.

Again, R-squared indicates how well the model fits the data. An R-squared of .64 is not as exciting as the R-squared I got for xK%; it means the model predicts about 64 percent of the fit, and 36 percent is explained by things I haven’t included in the model. Certainly, more variables could help explain xBB%. I am already considering combining FanGraphs’ PITCHf/x data with some of Baseball Reference‘s data, which does a great job of keeping track of the number of 3-0 counts, four-pitch walks and so on.

And again, for the reader to use the equation above to his or her benefit, one would plug in the appropriate values for a player in a given season or time frame and determine his xBB%. Then one could compare the xBB% to single-season or career BB% to derive some kind of meaningful results. And (one more) again, I have already taken the liberty of doing this for you.

Instead of including every pitcher from the sample, I narrowed it down to only pitchers with at least three years’ worth of data in order to yield some kind of statistically significant results. (Note: a three-year sample is a small sample, but three individual samples of 160+ innings is large enough to produce some arguably robust results.) “Avg BB% – xBB%” (or “diff%”) takes the average of a pitcher’s difference between actual and expected walk rates from 2010 to 2014. It indicates how well (or poorly) he performs compared to his xBB%: the lower a number, the better. This time, I included “t-score”, which measures how reliable diff% is. The key value here is 1.96; anything greater than that means his diff% is reliable. (1.00 to 1.96 is somewhat reliable; anything less than 1.00 is very unreliable.) Again, this is slightly problematic because there are five observations (years) at most, but it’s the best and simplest usable indicator of simplicity.

Thus, Mark Buehrle, Mike Leake, Hiroki Kuroda, Doug Fister, Tim Hudson, Zack Greinke, Dan Haren and Bartolo Colon can all reasonably be expected to consistently out-perform their xBB% in any given year. Likewise, Aaron Harang, Colby Lewis, Ervin Santana and Mat Latos can all reasonably be expected to under-perform their xBB%. For everyone else, their diff% values don’t mean a whole lot. For example, R.A. Dickey‘s diff% of +0.03% doesn’t mean he’s more likely than someone else to pitch exactly as good as his xBB% predicts him to; in fact, his standard deviation (StdDev) of 0.93% indicates he’s less likely than just about anyone to do so. (What it really means is there is only a two-thirds chance his diff% will be between -0.90% and +0.96%.)

As with xK%, I compiled a list of fantasy-relevant starters with only two years’ worth of data that see sizable fluctuations between 2013 and 2014. Their data, at this point, is impossible (nay, ill-advised) to interpret now, but it is worth monitoring.

Miller is an interesting case: he was atrociously bad about gifting free passes in 2014, but his diff% was only marginally worse than it was in 2013. It’s possible that he was a smart buy-low for the braves — but it’s also possible that Miller not only perennially under-performs his xBB% but is also trending in the wrong direction.

I’m not gonna lie, I have no idea why Cobb, Corey Kluber and others show up as only having one year of data when they have two in the xK% dataset. This is something I noticed now. Their exclusion doesn’t fundamentally change the model’s fit whatsoever because it did not rely on split-season data; I’m just curious why it didn’t show up in FanGraphs’ leaderboards. Oh well.

Implications: Richards and Roark perhaps over-performed. Meanwhile, it’s possible that Odorizzi, Ross and Ventura will improve (or regress) compared to last year. I’m excited about all of that. Richards will probably be pretty over-valued on draft day.

Here’s the second installment of my breakdown of spring training stats. You can view the first one by scrolling down like four inches to the previous post. Here is a look at a variety of pitchers in no particular order.

James Shields, KCImportant stats: 14.2 IP, 18 K, 0 BB (0.61 ERA, 0.48 WHIP)Why they’re important: Shields is firmly entrenched as a solid No. 2 fantasy starter, but he is off to as a hot a start as anyone right now, striking out 11.05 batters per nine innings and walking nobody. Not saying he’s worth bumping up in your rankings, but perhaps he’ll give you a little more than what you expected this year.

Max Scherzer, DETImportant stats: 14.1 IP, 16 K, 2 BBWhy they’re important: It would be unjust to exclude him. He’s having an excellent start, but he’s an excellent pitcher, so this is nothing extraordinary at this point.

Chris Tillman, BALImportant stats: 12.2 IP, 14 K, 2 BBJustin Masterson, CLEImportant stats: 13.0 IP, 14 K, 2 BBWhy they’re important: What’s the difference between them? Tillman has a 4.97 ERA and 1.26 WHIP while Masterson is sporting a 0.00 ERA and 0.62 WHIP. Meanwhile, their underlying stats are almost identical. This is where small sample sizes can really warp perspectives. Each guy is the victim and beneficiary of batting average on balls in play (BAbip), respectively. Only difference is Masterson is giving up fewer fly balls, making him less prone to home runs and hits.

Corey Kluber, CLEImportant stats: 14.1 IP, 15 K, 2 BBWhy they’re important: Maybe you’ve caught on to the trend again: I’m focusing on guys with excellent strikeout rates as well as strikeout-to-walk ratios (K/BB). Ignore the 5.02 ERA and 1.33 WHIP; Kluber’s BAbip is a sky-high .395 over this small sample size. He’s steal dealing. Also, he has the fifth-best ground ball rate of qualified spring training pitchers. I’ve read concerns about his home runs allowed last year. Can’t hit a home run on the ground, son. (Well, technically you can, but… shhhhhhh.)

Josh Johnson, SDImportant stats: 13.1 IP, 1.05 WHIP, 13 K, 4 BBWhy they’re important: For people hoping for a comeback, these ratios (8.78 K/9, 2.70 BB/9) are the makings of a solid starter. He’s not on my radar, but I acknowledge reasons why he could be on it (aside from the fact that he used to be one of the most dominant pitchers in all of baseball).

Alex Wood, ATLImportant stats: 14 IP, 0.00 ERA, 0.93 WHIP, 12 K, 2 BBWhy they’re important: He had a 1.73 ERA and 0.99 WHIP in the minors with a 3.78 K/BB. He followed it up with an 8.9 K/9 in the majors, nearly identical to his minor-league rate. The Braves develop great pitchers (and they know when to deal them… looking at you, Tommy Hanson). Wood is the next in line.

There are pitchers having bad springs, too. Guess which statistic I’m primarily using to evaluate them?

Tony Cingrani, CINImportant stats: 12.2 IP, 6.39 ERA, 1.42 WHIP, 13 K, 6 BBWhy they’re important: I’m not as concerned with the ratios as I am the walks, which he’s handing out at a 5.68 walks-per-nine-innings (BB/9) clip. Strikeouts are still there, which is good, and, of course, it’s worth acknowledging the small sample size. Maybe he’s working off the offseason slumber. But I’m keeping my eye on his control.

Tim Hudson, SF
Important stats: 13.1 IP, 1.58 WHIP, 9 BBWhy they’re important: Nothing matters here except for the lack of control. Cingrani’s walks are a bit disconcerting; Hudson’s walks (6.08 BB/9) is really worrisome, especially for an older pitcher coming back from a gruesome foot/ankle/leg injury. Perhaps it’s a bit early to predict the beginning of the end, but I’ll say it anyway: this could be the beginning of the end of Tim Hudson. It’s a shame, but it ultimately happens to everyone.

Matt Moore, TBImportant stats: 10.1 IP, 2.32 WHIP, 10 K, 11 BBWhy they’re important: He’ll always be loved for his strikeout propensity but his walk rate (9.58 BB/9) is most horrifying of all. I understand if you like him, but I will never draft him because of how he damages my WHIP — and a player with bad command is one bad-luck-BAbip away from having an absolutely miserable year.

Jose Quintana, CHWImportant stats: 6 IP, 30.00 ERA, 4.00 WHIPWhy they’re important: And the Worst/Most Humiliating Spring Training award goes to… Jose Quintana! Just look at it. It’s almost impossible how bad he’s been. But, in his defense, there’s a .586 BAbip at work here. And that, my friends, is why sample sizes this small should not be trusted. Some statistical anomalies are worth noting, but this one is simply outrageous. I am not changing my ranking of him based on this.

I said this verbatim in my last post: “Do your own research, form your own opinions.” It’s important to remember that these are incredibly small smaple sizes, meaning there’s a lot of volatility involved here. Still, some metrics can be very telling, and strikeout and walk rates can be much more indicative of future performance than ERA (or even WHIP, which can be jerked around by fluctuations in BAbip). Again, don’t put your eggs into one basket (where spring training stats is the basket in this analogy), but it’s worth remembering a name or two.

If FanGraphs were a home, or a hotel, or even a tent, I’d live there. I would swim in its oceans of data, lounge in its pools of metrics.

It houses a slew of PITCHf/x data — the numbers collected by the systems installed in all MLB ballparks that measure the frequency, velocity and movement of every pitch by every pitcher. It’s pretty astounding, but it’s also difficult for the untrainted eye to make something of the numbers aside from tracking the declining velocities of CC Sabathia‘s and Yovani Gallardo‘s fastballs.

I used linear regression to see how a pitcher’s contact, swinging strike and other measurable rates affect his strikeout percentage, and how that translates to strikeouts per inning (K/9). Ultimately, the model spits out a formula to generate an expected K/9 for a pitcher. I pulled data from FanGraphs comprised of all qualified pitchers from the last four years (2010 through 2013).

The idea is this: A pitcher who can miss more bats will strike out more batters. FanGraphs’ “Contact %” statistic illustrates this, where a lower contact rate is better. Similarly, a pitcher who can generate more swinging strikes (“SwStr %”) is more likely to strike out batters.

Using this theory coupled with the aforementioned data, I “corrected” the K/9 rates of all 2013 pitchers who notched at least 100 innings. Instead of detailing the full results, here are the largest differentials between expected and actual K/9 rates. (I will list only pitchers I deem fantasy relevant.)

There’s a lot to digest here, so I’ll break it down. It appears Perez was the unluckiest pitcher last year, of the ones who qualified for the study, notching almost 1.7 fewer strikeouts per nine innings than he would be expected to, given the rate of whiffs he induced. Conversely, rookie sensation Cingrani notched almost 2.2 more strikeouts per nine innings than expected.

There is a caveat. I was not able to account for facets of pitching such as a pitcher’s ability to hide the ball well, or his tendency to draw strikes-looking. With that said, a majority of the so-called lucky ones are pitchers who, in 2013, experienced a breakout (Cingrani, Fernandez, Miller, Darvish, Masterson, Tillman) or a renaissance (Jimenez, Kazmir, Masterson — woah, all Cleveland pitchers). Is it possible these pitchers can all repeat their performances — especially the ones who have disappointed us for years? Perhaps not.

(Update, Jan. 24: Cliff Lee’s mark of -1.86 is, amazingly, not unusual for him. Over the last four years, the average difference between his expected and actual K/9 rates is … drum roll … -1.88. Insane!)

Darvish and Liriano were in a league of their own in terms of inducing swings and misses, notching almost 30 percent each. (Anibal Sanchez was third-best with 27 percent. The average is about 21 percent.) However, Darvish recorded 2.78 more K/9 than Liriano. Is there any rhyme or reason to that? Darvish is, without much argument, the better pitcher — but is he that much better? I don’t think so. Darvish was expected to notch 10.41 K/9 given his contact rate. Any idea what his 2012 K/9 rate was? Incredibly: 10.40 K/9.

More big names produced equally interesting results. King Felix Hernandez recorded a career-best 9.51 K/9, but he was expected to produce something closer to 8.57 K/9. His rate the previous three years? 8.52 K/9.

Dan Haren didn’t produce much in the way of ERA in 2013, but he did see a much-needed spike in his strikeout rate, jumping above 8 K/9 for the first time since 2010. His expected 7.07 K/9 says otherwise, though, and it fits perfectly with how his K/9 rate was trending: 7.25 K/9 in 2011, 7.23 K/9 in 2012.

I think my models tend to exaggerate the more extreme results (most of which are noted in the lists above) because they could not account for intangibles in a player’s natural talent. However, they could prove to be excellent indicators of who’s due for regression.

Only time will tell. Maybe Jose Fernandez isn’t the elite pitcher we already think he is — not yet, at least.

————

Notes: The data almost replicates a normal distribution, with 98 of the 145 observations (67.6 percent) falling within one standard deviation (1.09 K/9) of the mean value (7.19 K/9), and 140 of 145 (96.6 percent) falling within two standard deviations. The median value is 7.27 K/9, indicating the distribution is very slightly skewed left.

I’ve done most of my analysis thus far on starting pitchers, so I’ll continue the trend. A lot of pitchers broke out (depending on your definition of the term) last year — I counted 13, give or take, amid the top 50 starting pitchers of ESPN’s Player Rater — which is an excellent indicator of how valuable drafting unknown arms can be to a successful fantasy season. Seasons are won and lost on the backs of sleeper picks, and it’s time to acknowledge that sleepers exist outside the top 75-or-so pitchers according to “the experts.” For example, Hisashi Iwakuma was ranked 76th of starting pitchers (263rd overall) in preseason rankings by the ESPN staff. Patrick Corbin and Julio Teheran, the latter of whom had an incredible spring training, did not crack the top 300 players.

But I digress. Not all that glimmers is gold. Likewise, not all breakout stars are true fantasy studs. Let’s look at the 13 players I counted among ESPN’s top 50 starters and assess what their 2014 seasons will look like.

Hisashi Iwakuma: LEGIT
Iwakuma is a borderline breakout candidate — a handful of owners, including me, converted him from streamer to permanent addition to our fantasy rotations in the latter half of 2012. He dominated then, and there was no reason to think he wouldn’t do it again, based on advanced metrics. Anyway, he’s legit, but he benefited from a remarkably low BAbip, so there could be sizable regression to Iwakuma’s ratios. I ranked him 32nd overall heading into 2014, but that’s more a floor than a ceiling.

Matt Harvey: LEGIT, but…Harvey just had surgery on his elbow, so don’t worry about drafting him — unless you’re in a keeper league, then make sure he doesn’t slip too far. You don’t need to draft him in the 16th round for him to be valuable next year. He may warrant a 10th-round pick in your keeper league, depending on your keeper rules and the format of your league (number of DL spots, etc.).

Mike Minor: LEGITIs this just going to be a list of guys who are all legit? Maybe. Minor is basically the same pitcher he has always been, but he cut down on his walk rate big-time. There’s no reason to think he’ll magically lose control; my projection accounts for it to an extent, so his No. 27 ranking could be an undervaluation.

Clay Buchholz: KINDA LEGIT
Buchholz has always had the skill set. Two problems: he benefited from very favorable BAbip and HR/FB rates, and he has never started more than 26 games in a season. The perennial injury risk coupled with potential regression reminiscent of Kris Medlen between 2012 and 2013 makes him someone not worth banking on again.

Homer Bailey: LEGIT
Bailey is, like Iwakuma, a late-2012 bloomer. I streamed him like crazy as I tried to meet my innings cap in 2012, including his first no-hitter (yes, I’m bragging), so his 2013 breakout is less surprising. I may be among a small crowd who thinks he’s a top-20 pitcher, though, but I don’t mind.

Patrick Corbin: KINDA LEGITCorbin is good, but he clearly petered out as the season concluded. I’m inclined to think he’s more the post-All Star Break pitcher than the pre-All Star Break one. I ranked him 36th, which accounts for his upside and downside.

Julio Teheran: KINDA LEGITGood, but not as good as he was. He’ll improve, but he’ll also regress, which will, for matters of simplicity, cancel out each other. He’s actually a very god pitcher, but he only earns the “kinda legit” label because I think he’s going to miss expectations for him, which seem to be pretty high right now. (Also, I have a quota to meet. C’mon, people!)

Justin Masterson: KINDA LEGITHe’s by no means an ace, but he did filthy things with his slider this year. It all depends on how often he uses it and if he’s able to retain its effectiveness next year. Still, he’s not much more than a back-end fantasy rotation kind of guy.

Chris Tillman: NOT LEGITThe 16 wins are more of a statistical anomaly than anything. He keeps improving his K’s per nine innings, but his value was fueled almost entirely by his win count. I’d be happy for him and his owners if he notched 12 next season.

Travis Wood: NOT LEGIT
It’s not fair to see these pitchers are “kinda legit” or “not legit” because they are all professionals, and damn good ones at that. That said, stat-heads waited all year for Wood’s BAbip to regress. It never did. Doesn’t mean it never will. Also, he plays for the Cubs. He’s simply not worth the bid or draft pick.

Ricky Nolasco: KINDA LEGITThe peripherals have always been there but he had seemingly suffered from bad luck — until last year, that is. The biggest question is if he’ll be able to replicate it. Yeah, why not? He plays for a good team in a pitcher’s park. The risk still exists, though, that the soon-to-be-31-year-old reverts to his incredibly hittable pre-2013 self.

Keep in mind that “kinda legit” pitchers are still worth drafting. Just try not to overpay too much.

Do research before your draft and find some sleepers of your own. A rookie pitcher always manages to wiggle his way into the Cy Young and/or Rookie of the Year conversation(s) — find one on which to gamble in 2014!

SEPT. 24 MARQUEE STREAM:Tyson Ross (SD) vs. ARI Ross, owned in only 10.2 percent of ESPN leagues after his dismal outing in Philadelphia (2/3 IP, 6 ER), returned to form in his next start, allowing only three hits and striking out seven across seven innings. I get why he’s not owned in more leagues — he’s 3-8 in 14 starts, which will turn away just about any fantasy owner — but let that bias help you. Ross has a 3.42 ERA, 1.17 WHIP and 8.5 K/9. Know who that trumps in all three categories? Derek Holland, Jorge De La Rosa, Kyle Lohse, Ricky Nolasco, Justin Verlander, Chris Tillman… and that’s just the beginning. Look, if you chase wins, then you’re going to live and die by that sword. But wins are hard to predict, and it’s safe to say Ross has gotten unlucky based on how well he has pitched and how the team for which he plays isn’t that bad at 72-83, good for third in the NL West (ahead of the Giants, no less). So, whatever. If you ignore him in this last week, you ignore him. But remember the name Tyson Ross as the final rounds of next year’s draft approach.

If I don’t go Ross, I may gamble on the St. Louis Cardinals’ Michael Wachaat home versus the Nationals. If you’re looking for strikeouts, Wacha (22.9 percent ESPN ownership) has ’em. People have been on and off the Wacha bandwagon after alternating good and bad starts, the most recent of which ended after 4-2/3 innings and 12 hits at Colorado. But he still struck out seven, and he still has a 3.21 ERA and 1.21 WHIP, and he still has massive upside. He’s not today’s best option, but I’d take him over Jason Vargas, Dan Straily or Doug Fister.

SEPT. 25 MARQUEE STREAM: Danny Salazar (CLE) vs. CHWHe’s 1-3 through nine starts, but it’s only by design. Salazar (14.2 percent ESPN ownership) had his pitch count lifted and he’s ready to humiliate more White Sox — last time he faced them, he struck out nine of them. Through 3-2/3 innings. THAT’S NINE STRIKEOUTS IN 11 TOTAL OUTS, PEOPLE. By the way, his line for the season stands at 3.09 ERA, 1.11 WHIP and 11.0 K/9 (with a 4.0 K/BB). If you need more proof as to why he’s so great, you can search for the love poems I’ve written him in the archives. (Disclaimer: There aren’t any Danny Salazar love poems in the archives. Yet.)

Some pitchers get better run support than others. It separates the fantasy studs from the fantasy duds, turns nobodies into somebodies and sometimes silences ace pitchers. Remember Cliff Lee‘s dismal 6-9 record last year despite his 3.05 ERA?

I won’t call them luckiest, for all these pitchers are plenty talented. So let’s say… run supportiest. Take a look at the run supportiest pitchers this year, followed by their average run support per game:

Max Scherzer, 7.64

Jeremy Hellickson, 6.70

Justin Verlander, 6.64

Anibal Sanchez, 6.57

Ryan Dempster, 6.38

Bartolo Colon, 6.22

Chris Tillman, 6.18

Matt Moore, 6.16

Lance Lynn, 6.00

Mike Minor, 6.00

Well, look at that. Mr. 15-game winner Max Scherzer is at the top of the list, and by no small margin. Without digging further, it’s important to make some distinctions. The average team scores approximately 4.20 runs per game, but no team is the average team. Although the Boston Red Sox lead the majors in scoring, it’s Scherzer’s own Detroit Tigers who lead in runs scored per game at 5.18 runs. It probably comes as no surprise that the Miami Marlins are last in runs scored at 3.19 per game, almost a full two runs fewer than the Tigers.

Part of the strategy in fantasy baseball is finding not necessarily the best pitchers but the above-average pitchers on good teams who will naturally get a lot of run support. Ryan Dempster isn’t having a great season by measure of his 4.54 ERA, but playing for the Red Sox certain bolsters his chances of collecting wins without having lights-out stuff. (Unfortunately, it hasn’t worked out that way for Dempster, notching only six wins.)

Instead of looking at the top 10 run supportiest pitchers in nominal terms, we ought to normalize the list by taking the difference between the pitchers’ run support and the average runs scored by their teams. The new list looks like this:

Max Scherzer, 2.46

Jeremy Hellickson, 2.09

Bartolo Colon, 1.77

Yovani Gallardo, 1.61

Matt Moore, 1.51

Hyun-Jin Ryu, 1.54

Chris Tillman, 1.50

Mike Minor, 1.47

Yu Darvish, 1.46

Justin Verlander, 1.46

The number following each name is the difference between the pitcher’s run support and his team’s average runs scored per game. Scherzer and Tampa Bay Rays pitcher Jeremy Hellickson lead the list again, but some new names popped up: Yovani Gallardo, Hyun-Jin Ryu and Yu Darvish. The 10 pitchers above have combined for 115 wins, or 11.5 wins on average. Even Gallardo has eight wins despite having the eighth worst ERA of all qualified starters.

This list serves two purposes, although both aren’t immediately valuable: 1) although most of these pitchers are pitching well, don’t be surprised if they win less often as their run support regresses toward the mean; 2) if you’re in a dynasty league. don’t bank on a potential 20-game winner to do it again next year, especially if he’s the beneficiary of randomly elevated run support.

In contrast, here are the 10 least run-supportiest pitchers (relative to average team run support like the previous list):

Chris Sale, -1.22

Homer Bailey, -1.19

Kris Medlen, -1.00

Eric Stults, -0.99

A.J. Burnett, -0.88

Joe Blanton, -0.82

Roberto Hernandez, -0.78

Julio Teheran, -0.75

John Lackey, -0.75

Travis Wood, -0.74

The above pitchers have combined for only 61 wins, or 6.1 wins on average, a far cry from 115 wins (11.5 average) posted by the top 10 run supportiest pitchers. These pitchers don’t throw for terrible teams, either — six of them play for contenders, or call it seven if you’re a hopeless Angels fan.

(Interjecting some notes: Red Sox starter John Lackey is having a renaissance season, and it looks like he has nobody but himself to thank for his seven wins; Chicago Cubs starter Travis Wood is having a breakout year despite a lack of run support; I just want a reason to say “the artist formerly known as Fausto Carmona”; if I’m in a dynasty league, I’m gunning for Cincinnati Reds starter Homer Bailey, who would be having a breakout season ,piggybacking on his very solid second half of 2012, if it were not for his miserable run support… he ought to have better stats to go with his 1.14 WHIP.)

My takeaway from all of this, again, is as much predictive as it is descriptive. If I had to offer bits of advice based on what I’ve presented some of it would be the following:

Buy low on A.J. Burnett, who is 4-7 with a sub-3.00 ERA playing for the NL Central-leading Pittsburgh Pirates…

Do the same for Lackey, who shows no signs of slowing down…

Sell high on Tampa Bay Rays pitcher Jeremy Hellickson, who is sporting a career-worst ERA and is being buoyed by his win total…

I’d even venture to say sell high on Los Angeles Dodgers pitcher Hyun-Jin Ryu and Baltimore Orioles pitcher Chris Tillman, who are both benefiting from high strand rates even amid seasons I would classify as underwhelming…

And I’d even sell high on Big Fat Bartolo Colon, who simply won’t keep winning every game and has a lackluster strikeout rate…

Remember these names during your draft next year! Run support can fluctuate randomly and wildly year to year. Just ask Cliff Lee.